Abstract
Cyberattacks are always a big threat for companies and organizations worldwide. The impact of the security breach affects the financial, reputational, and legal areas of the companies and organizations. So, the demand is high for a rapid solution to detect cyberattacks as early as possible. Advanced persistent threats (APTs) are sophisticated and targeted cyberattacks which have long persistence inside the network. During an APT, the attacker will expand its reach over the network. This stage is called lateral movement, which is the very important stage in APT. In this paper, the importance of identifying the APT in the early stage and how it can be detected using the machine learning approach is discussed.
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Chacko, A.A., Edwin, B., Thanka, M.R. (2022). Detecting the Lateral Movement in Cyberattack at the Early Stage Using Machine Learning Techniques. In: Peter, J.D., Fernandes, S.L., Alavi, A.H. (eds) Disruptive Technologies for Big Data and Cloud Applications. Lecture Notes in Electrical Engineering, vol 905. Springer, Singapore. https://doi.org/10.1007/978-981-19-2177-3_54
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DOI: https://doi.org/10.1007/978-981-19-2177-3_54
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